Amazon Web Services (AWS) recently announced the launch of AWS Glue Data Quality, a serverless solution designed to provide high-quality data management across data lakes and pipelines. As data lakes gain popularity, ensuring data quality becomes critical to prevent them from turning into data swamps. However, establishing data quality typically involves manual work and time-consuming processes. AWS Glue Data Quality streamlines the management of data quality and significantly reduces the time spent on manual tasks from several days to mere hours.
The service automates the computation of data statistics, suggests data quality rules, and monitors data, sending alerts when a drop in data quality is detected. Consequently, identifying missing, outdated, or incorrect data becomes more efficient, saving businesses from potential negative impacts. As part of the AWS Glue suite, AWS Glue Data Quality eliminates the need for infrastructure management and maintenance. The service automates the process of computing data statistics and recommending data quality rules, improving data freshness, accuracy, and integrity.
This automation not only reduces manual labor involved in data analysis and rule identification but also allows the use of predefined data quality rules. For an extensive list of supported rules, refer to the Data Quality Definition Language (DQDL). Moreover, AWS Glue Data Quality is accessible via multiple platforms, including AWS Glue Data Catalog, Glue Studio, and Glue Studio notebooks. This versatility empowers data stewards to set rules in the Data Catalog, while developers can build data integration pipelines using notebook-based interfaces. In addition, data engineers can submit jobs from their preferred code editor through interactive sessions.
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